
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012  Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_ORDERING_H
#define EIGEN_ORDERING_H

namespace Eigen {

#include "Eigen_Colamd.h"

namespace internal {

    /** \internal
  * \ingroup OrderingMethods_Module
  * \param[in] A the input non-symmetric matrix
  * \param[out] symmat the symmetric pattern A^T+A from the input matrix \a A.
  * FIXME: The values should not be considered here
  */
    template <typename MatrixType> void ordering_helper_at_plus_a(const MatrixType& A, MatrixType& symmat)
    {
        MatrixType C;
        C = A.transpose();  // NOTE: Could be  costly
        for (int i = 0; i < C.rows(); i++)
        {
            for (typename MatrixType::InnerIterator it(C, i); it; ++it) it.valueRef() = typename MatrixType::Scalar(0);
        }
        symmat = C + A;
    }

}  // namespace internal

/** \ingroup OrderingMethods_Module
  * \class AMDOrdering
  *
  * Functor computing the \em approximate \em minimum \em degree ordering
  * If the matrix is not structurally symmetric, an ordering of A^T+A is computed
  * \tparam  StorageIndex The type of indices of the matrix 
  * \sa COLAMDOrdering
  */
template <typename StorageIndex> class AMDOrdering
{
public:
    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;

    /** Compute the permutation vector from a sparse matrix
     * This routine is much faster if the input matrix is column-major     
     */
    template <typename MatrixType> void operator()(const MatrixType& mat, PermutationType& perm)
    {
        // Compute the symmetric pattern
        SparseMatrix<typename MatrixType::Scalar, ColMajor, StorageIndex> symm;
        internal::ordering_helper_at_plus_a(mat, symm);

        // Call the AMD routine
        //m_mat.prune(keep_diag());
        internal::minimum_degree_ordering(symm, perm);
    }

    /** Compute the permutation with a selfadjoint matrix */
    template <typename SrcType, unsigned int SrcUpLo> void operator()(const SparseSelfAdjointView<SrcType, SrcUpLo>& mat, PermutationType& perm)
    {
        SparseMatrix<typename SrcType::Scalar, ColMajor, StorageIndex> C;
        C = mat;

        // Call the AMD routine
        // m_mat.prune(keep_diag()); //Remove the diagonal elements
        internal::minimum_degree_ordering(C, perm);
    }
};

/** \ingroup OrderingMethods_Module
  * \class NaturalOrdering
  *
  * Functor computing the natural ordering (identity)
  * 
  * \note Returns an empty permutation matrix
  * \tparam  StorageIndex The type of indices of the matrix 
  */
template <typename StorageIndex> class NaturalOrdering
{
public:
    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;

    /** Compute the permutation vector from a column-major sparse matrix */
    template <typename MatrixType> void operator()(const MatrixType& /*mat*/, PermutationType& perm) { perm.resize(0); }
};

/** \ingroup OrderingMethods_Module
  * \class COLAMDOrdering
  *
  * \tparam  StorageIndex The type of indices of the matrix 
  * 
  * Functor computing the \em column \em approximate \em minimum \em degree ordering 
  * The matrix should be in column-major and \b compressed format (see SparseMatrix::makeCompressed()).
  */
template <typename StorageIndex> class COLAMDOrdering
{
public:
    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
    typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;

    /** Compute the permutation vector \a perm form the sparse matrix \a mat
      * \warning The input sparse matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
      */
    template <typename MatrixType> void operator()(const MatrixType& mat, PermutationType& perm)
    {
        eigen_assert(mat.isCompressed() &&
                     "COLAMDOrdering requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to COLAMDOrdering");

        StorageIndex m = StorageIndex(mat.rows());
        StorageIndex n = StorageIndex(mat.cols());
        StorageIndex nnz = StorageIndex(mat.nonZeros());
        // Get the recommended value of Alen to be used by colamd
        StorageIndex Alen = internal::Colamd::recommended(nnz, m, n);
        // Set the default parameters
        double knobs[internal::Colamd::NKnobs];
        StorageIndex stats[internal::Colamd::NStats];
        internal::Colamd::set_defaults(knobs);

        IndexVector p(n + 1), A(Alen);
        for (StorageIndex i = 0; i <= n; i++) p(i) = mat.outerIndexPtr()[i];
        for (StorageIndex i = 0; i < nnz; i++) A(i) = mat.innerIndexPtr()[i];
        // Call Colamd routine to compute the ordering
        StorageIndex info = internal::Colamd::compute_ordering(m, n, Alen, A.data(), p.data(), knobs, stats);
        EIGEN_UNUSED_VARIABLE(info);
        eigen_assert(info && "COLAMD failed ");

        perm.resize(n);
        for (StorageIndex i = 0; i < n; i++) perm.indices()(p(i)) = i;
    }
};

}  // end namespace Eigen

#endif
